Individual skill
colm-reproducibility
Use when hardening a COLM paper's reproducibility story — pinning open-weight checkpoints and tokenizers, handling API-model drift and deprecation honestly, versioning evaluation harnesses and prompts, disclosing compute, and writing availability statements that distinguish what is releasable from what is not.
COLM-Skills/skills/colm-reproducibility/SKILL.md
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# COLM Reproducibility
Reproducibility at a language-modeling venue is a different problem than at a
classical ML venue, because the central artifact — the model — is often either too
large to rerun or owned by someone else and changing weekly. COLM papers therefore
need a *layered* story: what any reader can redo, what a funded lab can redo, and
what will drift no matter what anyone does. Say which layer each claim lives in.
## The three reproducibility layers
| Layer | Who can redo it | Your obligation |
|---|---|---|
| Exact | Anyone with the artifact | Pinned checkpoint + harness + prompts + decoding → same numbers (within stated sampling noise) |
| Budgeted | A lab with comparable compute | Full training recipe: data, hyperparameters, schedule, hardware, wall-clock, known failure modes |